Health Insurance Fraud Detection and Characterization

在加拿大，每年的医疗保健费用为2150亿加元。欺诈和滥用占这一金额的2%至10%，每年造成约20亿美元的损失。欺诈的成本增加了处方药和疾病的成本。尽管欺诈性索赔的成本巨大，但大多数保险公司实施的欺诈检测过程 ** 在很大程度上是一个手动过程，领域专家可能会标记 ** 超过一定阈值的索赔，以便进一步审查。这些审查费用高昂，效率低下。领域专家可能花了很多时间，但通常只能识别一小部分欺诈性索赔。随着良性和欺诈性索赔的数量持续增长，迫切需要创新的解决方案来解决健康保险行业中这一重要而又具有挑战性的问题。Segic是一家总部位于哥伦比亚的公司，旨在提供创新的软件平台，以满足 ** 集体保险行业的需求。该研究项目是Segic战略计划的一部分，旨在利用人工智能（AI）来增强其提供的软件服务的能力。具体来说，这个为期6个月的 ** 项目旨在开发一种有效的机器学习方法来识别和描述欺诈性索赔。Segic将贡献他们识别和描述欺诈案件的专业知识。麦吉尔团队将 ** 贡献他们在机器学习和网络取证领域的知识。两个 ** 团队的联合力量将产生一个有效的健康保险欺诈检测引擎原型。研究结果将 ** 提高医疗保险行业欺诈检测的效率和有效性，从而 ** 有助于降低加拿大的医疗成本。

In Canada, the cost of healthcare is $215 billion annually. Fraud and abuse account for 2% to 10% of this**amount, translating into losses of approximately $2 billion per year. The cost of fraud increases the costs of**prescription drugs and illnesses. Despite the huge cost of fraudulent claims, the fraud detection process**implemented by most insurance companies is very much a manual process, where domain experts might flag**claims that go beyond a certain threshold for further review. These reviews are costly and inefficient. Domain**experts may have spent a lot of time, but often can identify only a small fraction of fraudulent claims. As the**volume of both benign and fraudulent claims continues to grow, there is a pressing need for innovative**solutions that can tackle this important yet challenging problem in the health insurance industry.**Segic is a Canadian-based company that aims at providing innovative software platforms to meet their needs of**the collective insurance industry. This research project is part of Segic's strategic plan to utilize artificial**intelligence (AI) to enhance the capabilities of their offered software services. Specifically, this 6-month**project aims at developing an effective machine learning method to identify and characterize fraudulent claims.**Segic will contribute their expertise of identifying and characterizing fraudulent cases. The McGill team will**contribute their knowledge in the areas of machine learning and cyber forensics. The joint force of the two**teams will result in an effective health insurance fraud detection engine prototype. The research result will**enhance the efficiency and effectiveness of the fraud detection in the health insurance industry, which in turn**contributes to the reduction of healthcare cost in Canada.